Pixel and object-based land cover mapping and change detection from 1986 to 2020 for Hungary using histogram-based gradient boosting classification tree classifier

  • András Gudmann University of Szeged
  • László Mucsi University of Szeged
DOI: https://doi.org/10.5937/gp26-37720
Keywords: land use, land cover, image classification, change detection, gradient boosting

Abstract


The large-scale pixel-based land use/land cover classification is a challenging task, which depends on many circumstances. This study aims to create LULC maps with the nomenclature of Coordination of Information on the Environment (CORINE) Land Cover (CLC) for years when the CLC databases are not available. Furthermore, testing the predicted maps for land use changes in the last 30 years in Hungary. Histogram-based gradient boosting classification tree (HGBCT) classifier was tested at classification. According to the results, the classifier, with the use of texture variance and landscape metrics is capable to generate accurate predicted maps, and the comparison of the predicted maps provides a detailed image of the land use changes.

References


  • Bezdan, A., Vranešević, M., Blagojević, B., Pejić, B., Bezdan, J., Milić, D., Tica, N., & Zekić, V. (2019). Agricultural Drought Risk Assessment in Vojvodina. In Z. Ladányi & V. Blanka (Eds.), Monitoring, risks and management of drought and inland excess water in South Hungary and Vojvodina (pp. 48–62). University of Szeged Department of Physical Geography and Geoinformatics Projekt.

  • Bui, D. H., & Mucsi, L. (2021). From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules. Remote Sensing, 13(9). https://doi.org/10.3390/rs13091700

  • Buttner, G., & Kosztra, B. (2017). CLC2018 Technical Guidelines.

  • Choudhury, K., & Jansen, L. (1999). Terminology for Integrated Resources Planning and Management.

  • Comber, A. J., Birnie, R. V., & Hodgson, M. (2000). Using landscape metrics to model land cover change. In T. Clare & D. Howard (Eds.), 9th annual conference of the international-association-for-landscape ecology (pp. 143–161). Proceedings of the International Association of Landscape Ecology (UK) Conference: Quantitative approaches to Landscape Ecology.

  • Csikós, N., & Szilassi, P. (2021). Modelling the Impacts of Habitat Changes on the Population Density of Eurasian Skylark (Alauda arvensis) Based on Its Landscape Preferences. Land, 10(3). https://doi.org/10.3390/land10030306

  • Emery, W., & Camps, A. (2017). The History of Satellite Remote Sensing. In W. Emery & A. Camps (Eds.), Introduction to Satellite Remote Sensing (pp. 1–42). Elsevier. https://doi.org/10.1016/B978-0-12-809254-5.00001-4

  • European Commission - DG Agriculture and Rural Development, F. E. U. (2020). Statistical Factsheet - Hungary.

  • Feranec, J., Jaffrain, G., Soukup, T., & Hazeu, G. (2010). Determining changes and flows in European landscapes 1990–2000 using CORINE land cover data. Applied Geography, 30(1), 19–35. https://doi.org/10.1016/j.apgeog.2009.07.003

  • Friedman, J. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29, 1189–1232. https://doi.org/10.2307/2699986

  • Friedman, J. (2002). Stochastic Gradient Boosting. Computational Statistics & Data Analysis, 38, 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2

  • Gudmann, A., Csikós, N., Szilassi, P., & Mucsi, L. (2020). Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics. Remote Sensing, 12(21), 3580. https://doi.org/10.3390/rs12213580

  • Heymann, Y., Steenmans, C., Croisille, G., Bossard, M., Lenco, M., Wyatt, B., Weber, J.-L., O’Brien, C., Cornaert, M.-H., & Sifakis, N. (1994). Corine Land Cover Technical Guide. Office for Official Publications of the European Communities.

  • Hungarian Central Statistical Office. (2019). Gazetteer of Hungary, 1 January 2019 (B. Pál (ed.); 1st ed.). Hungarian Central Statistical Office.

  • Hungarian Central Statistical Office. (2020). Statistical Pocketbook of Hungary, 2019 (V. G. Dr. Bódiné (ed.)). Hungarian Central Statistical Office.

  • Malinowski, R., Lewiński, S., Rybicki, M., Gromny, E., Jenerowicz, M., Krupiński, M., Nowakowski, A., Wojtkowski, C., Krupiński, M., Krätzschmar, E., & Schauer, P. (2020). Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sensing, 12(21), 3523. https://doi.org/10.3390/rs12213523

  • Mari, L., & Mattányi, Z. (2002). Egységes európai felszínborítási adatbázis a CORINE Land Cover program (A uniform european land cover database the CORINE Land Cover program). Földrajzi Közlemények, 76 (50), 31–38.

  • McCarty, D. A., Kim, H. W., & Lee, H. K. (2020). Evaluation of Light Gradient Boosted Machine Learning Technique in Large Scale Land Use and Land Cover Classification. Environments, 7(10). https://doi.org/10.3390/environments7100084

  • Mezősi, G. (2017). The Physical Geography of Hungary. Springer International Publishing.

  • Steurer, M., & Bayr, C. (2020). Measuring urban sprawl using land use data. Land Use Policy, 97, 104799. https://doi.org/10.1016/j.landusepol.2020.104799

  • Szilassi, P. (2017). Land cover variability and the changes of land cover pattern in landscape units of Hungary. Journal of Landscape Ecology, 15, 131–138.

  • Tobak, Z., Leeuwen van, B., Kovács, F., & Szatmári, J. (2019). High precision mapping and monitoring of inland excess water inundations. In Z. Ladányi & V. Blanka (Eds.), Monitoring, risks and management of drought and inland excess water in South Hungary and Vojvodina (pp. 13–23). University of Szeged Department of Physical Geography and Geoinformatics.

  • Townshend, J., Justice, C., Li, W., Gurney, C., & McManus, J. (1991). Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sensing of Environment, 35(2), 243–255. https://doi.org/10.1016/0034-4257(91)90016-Y

  • U.S.Geological Survey. (2012). Landsat: A global land-imaging mission. In Fact Sheet. https://doi.org/10.3133/fs20123072

  • Volker, A., Mezősi, G., & Mucsi, L. (1998). Die Pußta. Historisch-geographische und geoökologische Aspekte eines schulgeographischen und touristischen Leitbildes von Ungarn in Ungarn. In Natur - Raum - Geselschaft (pp. 175-217.). Johann Wolfgang Goethe-Universität Frankfurt.

  • Wulder, M. A., Coops, N. C., Roy, D. P., White, J. C., & Hermosilla, T. (2018). Land cover 2.0. International Journal of Remote Sensing, 39(12), 4254–4284. https://doi.org/10.1080/01431161.2018.1452075

  • Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., Allen, R. G., Anderson, M. C., Belward, A. S., Cohen, W. B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J. D., Hostert, P., Hughes, M. J., … Zhu, Z. (2019). Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127–147. https://doi.org/10.1016/j.rse.2019.02.015

  • Wulder, M. A., White, J. C., Loveland, T. R., Woodcock, C. E., Belward, A. S., Cohen, W. B., Fosnight, E. A., Shaw, J., Masek, J. G., & Roy, D. P. (2016). The global Landsat archive: Status, consolidation, and direction. Remote Sensing of Environment, 185, 271–283. https://doi.org/10.1016/j.rse.2015.11.032

  • Zhou, T., Li, Z., & Pan, J. (2018). Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification. Sensors, 18(2), 373. https://doi.org/10.3390/s18020373


 

Published
2022/10/14
Issue
Vol. 26 No. 3 (2022): Special Issue - “Natural Hazards and the Mitigation of their Impact”
Section
Original Research